Conference PaperPDF Available

Digital Technology in Architecture, Engineering, and Construction (AEC) Industry: Research Trends and Practical Status toward Construction 4.0


Abstract and Figures

Construction 4.0 represents the exploration of new technologies in the architecture, engineering and construction (AEC) industry. The development of digital technologies is rapid and the adoption of them significantly impacts construction projects, for example leading to a reduction in change orders, better decision making, and improvements in quality of work. However, stakeholders may find it challenging to determine the promising technologies within the context of the AEC industry. This paper presents an exploratory study to investigate the potentially applicable technologies and their research and practice trends in the AEC industry. A scoping review was the method utilized to perform a quantitative analysis of over five thousand journals papers published from 2010 onwards based on two academic databases (Scopus and CNKI). The results present the top 10 construction 4.0 technologies, including building information modelling (BIM), artificial intelligence (AI), 3D printing, machine learning, internet of things (IoT), geographic information systems (GIS), virtual reality (VR), big data, robotics and augmented reality (AR). Subsequently, 145 industry professionals were invited to select the most used construction 4.0 technologies in their projects via a questionnaire survey. Mobile devices, BIM and Digital signature are mostly adopted on-site. The findings of this study can enhance the awareness of stakeholders towards construction 4.0 technologies and may help them make better decisions in selecting and implementing the promising technologies.
Content may be subject to copyright.
Digital Technology in Architecture, Engineering, and Construction (AEC) Industry:
Research Trends and Practical Status toward Construction 4.0
Kaiyang Wang1; Fangyu Guo2; Cheng Zhang3; Jianli Hao4; and Dirk Schaefer5
1Ph.D. Student, Dept. of Civil Engineering, Xi’an Jiaotong-Liverpool Univ., China.
2Assistant Professor, Dept. of Civil Engineering, Xi’an Jiaotong-Liverpool Univ., China
(corresponding author). Email:
3Associate Professor, Dept. of Civil Engineering, Xi’an Jiaotong-Liverpool Univ., China.
4Senior Associate Professor, Dept. of Civil Engineering, Xi’an Jiaotong-Liverpool Univ., China.
5Professor, Dept. of Civil Engineering and Industrial Design, Univ. of Liverpool, UK.
Construction 4.0 represents the exploration of new technologies in the architecture,
engineering, and construction (AEC) industry. The development of digital technologies is rapid
and the adoption of them significantly impacts construction projects, for example, leading to a
reduction in change orders, better decision making, and improvements in quality of work.
However, stakeholders may find it challenging to determine the promising technologies within
the context of the AEC industry. This paper presents an exploratory study to investigate the
potentially applicable technologies and their research and practice trends in the AEC industry. A
scoping review was the method utilized to perform a quantitative analysis of over five thousand
journals papers published from 2010 onward based on two academic databases (Scopus and
CNKI). The results present the top 10 construction 4.0 technologies, including building
information modelling (BIM), artificial intelligence (AI), 3D printing, machine learning, internet
of things (IoT), geographic information systems (GIS), virtual reality (VR), big data, robotics,
and augmented reality (AR). Subsequently, 145 industry professionals were invited to select the
most used construction 4.0 technologies in their projects via a questionnaire survey. Mobile
devices, BIM, and Digital signature are mostly adopted on-site. The findings of this study can
enhance the awareness of stakeholders toward construction 4.0 technologies and may help them
make better decisions in selecting and implementing the promising technologies.
KEYWORDS: BIM, Digital technology, Technology adoption, Questionnaire, AEC industry
Many industries will change as a result of the technical development towards the fourth
industrial revolution. Digital technologies are explored within Industry 4.0 by the manufacturing
industry to find beneficial effects and improve productivity. Construction 4.0 is the equivalent of
exploring new technologies in the architecture, engineering, and construction (AEC) industry
(Craveiroa et al. 2019). The AEC industry is an important contributor to China's national
economy. In 2020, the total value of the industry reached 26.4 trillion RMB, with an increase of
Construction Research Congress 2022 983
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
6.2% over the previous year, which accounted for 7.2% of Gross Domestic Product (GDP)
(National Bureau of Statistics 2021). However, the number of practitioners has decreased and the
expense of the construction workforce has increased for two consecutive years in the industry
(CCIA 2021). Therefore, the Chinese government proposed to speed up the digital
transformation from the traditional paper-based approach in the AEC industry in the country’s
fourteenth five-year plan, in order to mitigate the issues such as loss of labour, carbon dioxide
emission and cost overrun. At the same time, digital devices, methods, and systems (also known
as digital technologies) have been developed vigorously, which provide strong support for the
digital transformation of new construction projects. By adopting digital technologies, enormous
opportunities could be introduced for enhancing the effectiveness and quality of construction
processes and creating business innovation. For example, the AEC industry is experiencing an
unprecedented reform of tools and methodology because of the incorporation of building
information modelling (BIM), integrated project management (IPD) and lean construction. Also,
the productivity of the AEC industry could be greatly improved if new technologies are
leveraged more effectively. Significant managerial and technological advancements have been
observed due to the technology application in the AEC industry (Sepasgozar et al. 2018).
Nevertheless, many studies have indicated that the AEC industry is reluctant to implement
emerging technologies, which shows lower levels of technology maturity than other industries
such as manufacturing, electronics and aviation (Pärn and Edwards 2017). Compared to other
industries, Oesterreich and Teuteberg (2016) stated that the AEC industry is more difficult to
manage and integrate new technologies, because construction projects are complex, requiring
individual undertakings and a high level of professional knowledge. They also concluded that
some challenges and problems should be taken into account before the digital transformation of
the industry, such as organizational and process changes, resistance to technological change,
hesitation to adopt technologies, and others. For instance, a large amount of small and medium-
sized enterprises (SMEs) hesitated to invest in new technologies due to the low awareness of the
benefits and the trends of the technology application. At present, it is impossible and
inappropriate to adopt various technologies in companies due to the large investments. It is
worthwhile to identify the digital technologies that have big potential for the current and future
AEC industry to improve the awareness of these technologies.
Thus, this paper aims to fill a gap in literature by providing an overview of the current
research and practical trend of digital technologies in the AEC industry. In order to achieve this
aim, the following objectives arise: (1) To identify digital technologies that are currently
associated with the concept of construction 4.0; (2) to investigate the research trend of these
technologies in the AEC industry; (3) to point out the current status of these technologies
implemented in the Chinese AEC industry.
Based on these questions, the findings of this study will enhance the awareness of
researchers, government policymakers, and industry stakeholders of current promising
technologies and direct them towards digital transformation.
This study employed literature and industrial questionnaire survey to provide a
comprehensive insight into the research trend and current application status of digital
technologies in the AEC industry. The process for this study is illustrated in Figure 1.
Construction Research Congress 2022 984
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
Figure 1. Research design
State of the art
The technological stream constitutes a significant research field concerning digital
transformation. Peculiar technologies can lead to important improvements in technique and
organization. Nevertheless, there are some inconsistencies of technologies list among the
different domains. In order to identify the current and potential technologies used in the AEC
industry, a preliminary search of existing literature was conducted. Multiple databases, including
Scopus, Web of Science, and specific publishers such as the American Society of Civil
Engineers (ASCE) were employed to search for and identify useful publications related to the
digital technologies in the industry. The scoping review technique of the Literature-Based
Discovery (LBD) approach (Levac, Colquhoun and O'Brien 2010) was adopted at this stage,
suitable for overviews of key concepts and research gaps of emerging areas. Following the
scoping review steps derived from the framework, relevant studies were selected. Twelve full-
text papers were included in the final review after data characterization of the searched journal
articles, identifying 65 related technologies.
Analysis of trends in existing research is helpful in identifying popular topics and exploring
future research direction. To investigate the number of research articles related to each
technology in the field of construction industry, the Scopus database was selected as the basis for
this investigation since it has comprehensive coverage of high-quality peer-reviewed articles and
the availability of more recent publications (Zhao et al. 2019). In order to identify the latest
research trend reported in the literature, the published papers are confined to the last ten years,
from 2010 to present (as of 29 April 2021). Two selection criteria were applied to filter the
articles: (1) The searched technology is the main research topic employed in the article, this is
because some studies only mentioned the technology such as 5G, whilst did not actually
investigate 5G in the article; and (2) the application of this technology is related to AEC
industry. Table 1 summarizes the frequency of occurrence for the top ten technologies
investigated in worldwide journal publications. Due to space limitations, an overview of each
specific technology is not possible, the readers are directed to the website
( for additional
Furthermore, to reflect the current research trend of these technologies in China, the core
Chinese journal papers published were also screened based on the same steps. CNKI database
Construction Research Congress 2022 985
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
was selected as the data source for its wide adoption in China. Firstly, a pilot screening was
conducted to search the target papers, showing that a limited number of papers were published
from 2010 to 2017, hence, only the papers published after 2018 were counted here. Figure 2
shows the frequency of occurrence for the top ten technologies investigated in the selected
Chinese database published from 2018 to present.
Survey instrument
A structured survey questionnaire was designed to investigate the current status of applying
the identified technologies on construction site. The survey contained four sections: (1)
objectives of the study along with a consent form; (2) demographic background of participants;
(3) participant’s experience level on the identified technologies; and (4) recommendation of the
promising technologies that should be invested in the AEC industry. In terms of section three,
the experience level of each technology was rated on a five-point Likert scale from 1 (very low
level) to 5 (very high level). The relative importance index (RII) method was employed to
calculate the experience level with each technology. This technique is utilized to determine the
importance of each indicator compared with others (Metro et al. 2021). The RII equation is
presented below:
× = 
() (0RII1)
Where W is scale weight from 1 to 5, value given to each technology by the respondents, A
is highest weight given (=5), and N is total number of respondents.
Concerning the survey sample, the unit of analysis in this survey refers to the employees in
the Chinese AEC industry. A Chinese website ( providing online
survey services was utilized to create the online questionnaire. Firstly, a pilot testing with five
experts from academia and industry was carried out to review the technologies and the
readability of the questionnaire. Minor modifications were made in response to the feedback
This research is exploratory, hence, a nonprobability purposive sampling technique was
employed for this study. Subsequently, the authors distributed 375 online questionnaires via
Email, LinkedIn or WeChat (communication software developed by Tencent) to the target
respondents. A total of 167 (44%) responses were received and all of them were complete
without missing data because of the online survey settings. Then, the repetitive responses with
the same Internet Protocol (IP) address were eliminated, and invalid responses with the same
rating for the Likert chart were discarded. Consequently, 145 valid samples were utilized for
further analysis.
State of the art – publication growth trends
This study identified sixty-five digital technologies that could be implemented in the
construction industry. Among them, Table 1 presents the top ten technologies that have been
studied over the past decade, including BIM, Artificial Intelligence (AI), 3D Printing, Machine
Learning, Internet of Things (IoT), Geographic Information system (GIS), Virtual Reality (VR),
Big Data, Robotics and Augmented Reality (AR). Each of them is briefly introduced below.
Construction Research Congress 2022 986
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
BIM: BIM is developed to act as a powerful tool to support effective information sharing and
communication among stakeholders in the construction industry in the whole life cycle of
building projects. Meanwhile, BIM-based project delivery establishes a paradigm for enhancing
project efficiency (Tang et al. 2019).
AI: AI refers to the ability of machines that mimic human intelligence to solve complicated
and ill-defined problems intelligently and adaptively by using algorithms. To promote the real
digital strategies in the construction industry, AI serves as the backbone to change the way a
construction project performs (Pan and Zhang 2021).
3D printing: 3D printing (also known as additive manufacturing) is an advanced
manufacturing process of joining materials to make solid objects automatically from 3D model
data without any dies, fixtures and tooling (Duballet, Baverel and Dirrenberger 2017). This
technology has become quite popular in industrial design, transportation, and aeronautics to
create functional prototypes because of its efficiency and minimum material wastage.
Machine learning: Machine learning, a subset of AI, makes systems evolve as if they are
learning, utilizing algorithms based on existing data to predict future behaviour. During
construction projects, it supports the automation of decision making in processes, logistics and
safety (Tixier et al. 2016).
IoT: IoT represents the interconnection of sensing and actuating devices to capture data and
enable information sharing via a unified framework (Khanna and Kaur 2019). In the construction
industry, IoT is able to connect BIM with physical devices for on-site control and monitoring,
optimizing communication and logistics (Tang et al. 2019).
GIS: GIS is a digital platform to store, manage and analyze different types of data and
present their spatial location in maps (Zagvozda et al. 2019). It can be applied for scoping and
preliminary study, visualization, and maintenance purpose in construction projects.
VR: VR can generate scenes with a realistic appearance from computer systems with glasses
or CAVE (cave automatic virtual environment). Currently, VR is commonly integrated with BIM
to improve safety and quality management. It can also be used as a design tool to allow clients
and end-users to immerse themselves inside their future built asset.
Big data: Three main attributes (i.e., volume, variety and velocity), also known as 3V, are
used to define big data. Construction data is well explained by big data since it is typically large,
dynamic and heterogeneous. By using big data techniques and technologies, large volumes of
information generated by the construction could be explored.
Robotics: The robotic technology can be grouped into four general categories in construction,
namely, on-site automated vehicles, off-site prefabrication systems, exoskeletons, and drones and
autonomous (Delgado et al. 2019). The robot could help with element cutting, structural
reinforcement bending, welding and other tasks (Tavares et al. 2019).
AR: AR is used to integrate images of virtual objects into the real world. It could satisfy the
goal of enhancing users' perception of virtual prototyping with real entities by inserting the
virtually simulated prototypes into the real environment (Li et al. 2018).
In addition to the technologies presented in the table, there is a significant rising trend of
investigating blockchain, cyber-physical systems (CPS) and digital twin in the research articles
in the past four years (2017 to 2020), with an annual growth rate of 273%, 132% and 127%,
respectively. Moreover, more than 32% of the reviewed articles come from six academic
journals, including Automation in Construction, Journal of Construction Engineering and
Management, Journal of Information Technology in Construction, Sustainability, Advanced
Engineering Informatics and Engineering, Construction and Architectural Management.
Construction Research Congress 2022 987
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
Moreover, CNKI database was employed as the analysis object to investigate the research
trend of the top ten technologies studied in China (as shown in Figure 2). Although the rankings
of specific technologies are slightly different from Table 1, 90% of the technologies that
appeared in Figure 2 are also included in Table 1, except for AR and UAV. UAV refers to small-
scale remote-controlled aerial vehicles equipped with sensors and cameras. The application of
UAV technology could improve safety planning and monitoring processes on construction site
since it can access specifically inaccessible, hard-to-reach, or unsafe locations (Martinez,
Gheisari and Alarcón 2020).
Based on the data extracted from these two databases, it can be observed that BIM has
become the core technology in the construction industry at the current stage, since BIM could
improve the management process and integrate with other tools to enhance its functionality. For
instance, with the integration of BIM and prefabrication, the field hospitals could be built ultra-
rapidly to combat the COVID-19 pandemic (Luo et al. 2020).
Table 1. Frequency of occurrence for the top ten digital technologies (Scopus)
3D printing
4 Machine
1 0 2 1 2 3 8 9 25 48 89 46 234
Big data
Figure 2. Frequency of occurrence for the top ten digital technologies (CNKI)
Construction Research Congress 2022 988
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
Survey results and analysis
A total of 145 valid responses were generated to investigate the current application status of
digital technologies. The respondents have at least three years of working experience in the AEC
industry, including civil engineer (51%), construction/project manager (41%) and consultant
The RII results are presented in Table 2, showing the most used digital technologies. Three
technologies have occurred in Table 1 and Figure 2, which are BIM, UAV and GIS. Mobile
devices, Digital signature, Electronic Document Management (EDM) systems, Global
Positioning System (GPS), 5G, Radio Frequency Identification (RFID) and Terrestrial Laser
Scanning (TLS) are other representative technologies, which are briefly introduced below.
Table 2. RII analysis results of the top ten used technologies
Technology Total respondents
1 2 3 4 5 RII Rank
Mobile devices
Digital signature
Note: 1: Very low level; 2: Low level; 3: Moderate level; 4: High level; 5: Very high level.
Mobile devices: They provide convenience and flexibility for construction tasks. The
commonly adopted mobile devices contain smartphones, iPads, field tablets, laptops, and others.
Digital signature: Digital signature refers to digitally encrypted/decryption electronic
signatures, including identities, time and date, and a password. The review and approval process
of the submitted electronic documents could be accelerated by using it.
EDM systems: EDM systems enable all electronic documents to be stored, updated, and
shared through a web server in a project (Guo, Jahren and Turkan 2018).
GPS: GPS provides services for positioning, navigation and timing (PNT). On construction
projects, GPS could locate and track the position of workers and machines, to improve safety
instruction (Tang et al. 2019).
5G: It is the fifth generation of wireless systems or mobile networks technology, with
decreased latency for a larger number of connected devices and increased average bandwidth
speed (Gupta et al. 2019).
RFID: RFID typically consists of three components, namely, RFID tags, RFID antennas and
a host computer. It could be applied for tracking and identifying moving objects between
different positions.
TLS: TLS, also known as terrestrial or topographic LiDAR (light detection and ranging), is
utilized to measure the distance from the device to the target and acquire 3D point cloud of the
Construction Research Congress 2022 989
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
scanned objects, which can be applied for geometric quality assessment due to its high speed and
accuracy (Rashidi et al. 2020).
It is reasonable that most trending technologies in research are not on this list (Table 2) since
it takes time for people to learn the right skills to apply and use the technologies effectively and
for industry enterprises to invest them.
An open question was raised to ask participants to recommend the promising digital
technologies that should be invested in the AEC industry. Nine technologies have over 5%
occurrence frequency (see Figure 3). Accordingly, BIM (21%) is the most recommended
technology by participants, followed by 5G, UAV, Robotics, GPS, EDM, 3D printing, IoT and
Figure 3. Recommendation of key technologies for investment
Digital transformation is inevitable in the AEC industry. By using different digital
technologies, greater efficiency, safety and productivity could be achieved for construction
projects. Nevertheless, the rapid change and updating of technologies put tremendous pressure
on stakeholders in terms of selecting and adopting the new technologies. This paper investigates
the research and practical trends of digital technologies in the AEC industry, which could serve
as the basis for organizations to determine their future direction and strategies associated with
technology implementation.
In this study, multiple databases were utilized to identify 65 technologies applied in the AEC
industry. Based on the Scopus database, the top ten technologies that have been mostly
researched from 2010 to 2021 are presented, including BIM, AI, 3D Printing, Machine Learning,
IoT, GIS, VR, Big Data, Robotics and AR. Meanwhile, the research trend of the technologies in
China was explored by using the CNKI database. The top ten technologies investigated in
Chinese academia are also presented and compared with the previous top ten list, where nine out
of ten technologies appear in both of the lists. Overall, BIM is the technology that has been
mostly studied in the field of civil engineering.
Based on the survey results, the most used technologies in the Chinese AEC industry are
Mobile devices, BIM, Digital signature, EDM, UAV, GPS, GIS, 5G, RFID and TLS. From the
perspective of participants, BIM, 5G, UAV, Robotics, GPS, EDM, 3D printing, IoT and RFID
are the promising technologies that should be invested in the AEC industry.
Construction Research Congress 2022 990
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
Finally, digitalization is not only about technologies, it is also about processes, organization
and people. The successful implementation of new technologies could be affected by various
factors from multiple perspectives such as socio-economic (e.g., governance mode),
psychological (e.g., behavioural intention) and managerial (e.g., top-management support).
Further research and development in some specific technologies are still necessary, and this
paper has provided a clear direction of research trend. Once stakeholders have realized the full
potential of digital technologies, the future AEC industry will be reshaped and brought into a
construction 4.0 era.
CCIA (China Construction Industry Association). (2021). Statistical Analysis of Construction
Industry Development in 2020. <>(accessed 5 May 2021).
Craveiroa, F., Duartec, J. P., Bartoloa, H., and Bartolod, P. J. (2019). Additive manufacturing as
an enabling technology for digital construction: A perspective on Construction 4.0.
sustainable development, 4, p.6.
Delgado, J. M. D., Oyedele, L., Ajayi, A., Akanbi, L., Akinade, O., Bilal, M., and Owolabi, H.
(2019). “Robotics and automated systems in construction: Understanding industry-specific
challenges for adoption.” Journal of Building Engineering, 26, p.100868.
Duballet, R., Baverel, O., and Dirrenberger, J. (2017). “Classification of building systems for
concrete 3D printing.” Automation in Construction, 83, pp.247-258.
Guo, F., Jahren, C. T., and Turkan, Y. (2018). “Electronic Document Management Systems for
the Transportation Construction Industry.” International Journal of Construction Education
and Research, 17(1), pp.52-67.
Gupta, R., Tanwar, S., Tyagi, S., and Kumar, N. (2019). “Tactile internet and its applications in
5g era: A comprehensive review.” International Journal of Communication Systems, 32(14),
Khanna, A., and Kaur, S. (2019). “Evolution of Internet of Things (IoT) and its significant
impact in the field of Precision Agriculture.” Computers and electronics in agriculture, 157,
Levac, D., Colquhoun, H., and O’Brien, K. K. (2010). “Scoping studies: advancing the
methodology.” Implementation science, 5(1), pp.1-9.
Li, X., Yi, W., Chi, H. L., Wang, X., and Chan, A. P. (2018). “A critical review of virtual and
augmented reality (VR/AR) applications in construction safety.” Automation in Construction,
86, pp.150-162.
Luo, H., Liu, J., Li, C., Chen, K., and Zhang, M. (2020). “Ultra-rapid delivery of specialty field
hospitals to combat COVID-19: Lessons learned from the Leishenshan Hospital project in
Wuhan.” Automation in Construction, 119, p.103345.
Martinez, J. G., Gheisari, M., and Alarcón, L. F. (2020). “UAV integration in current
construction safety planning and monitoring processes: Case study of a high-rise building
construction project in Chile.” Journal of Management in Engineering, 36(3), p.05020005.
Metro, K., Harper, C., and Bogus, S. M. (2021). “Factors Affecting Workforce Resilience in
Public Transportation Agencies.” Journal of Management in Engineering, 37(4),
National Bureau of Statistics. (2021). China National Economic and Social Development
Statistics Bulletin 2020. <>(accessed 5 May 2021).
Construction Research Congress 2022 991
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
Oesterreich, T. D., and Teuteberg, F. (2016). “Understanding the implications of digitization and
automation in the context of Industry 4.0: A triangulation approach and elements of a
research agenda for the construction industry.” Computers in industry, 83, pp.121-139.
Pan, Y., and Zhang, L. (2021). “Roles of artificial intelligence in construction engineering and
management: A critical review and future trends.” Automation in Construction, 122,
Pärn, E. A., and Edwards, D. J. (2017). “Conceptualizing the FinDD API plug-in: A study of
BIM-FM integration.” Automation in Construction, 80, pp.11-21.
Rashidi, M., Mohammadi, M., Sadeghlou Kivi, S., Abdolvand, M. M., Truong-Hong, L., and
Samali, B. (2020). “A decade of modern bridge monitoring using terrestrial laser scanning:
Review and future directions.” Remote Sensing, 12(22), p.3796.
Sepasgozar, S. M., Davis, S. R., Li, H., and Luo, X. (2018). “Modeling the implementation
process for new construction technologies: Thematic analysis based on Australian and US
practices.” Journal of Management in Engineering, 34(3), p.05018005.
Tang, N., Hu, H., Xu, F., and Zhu, F. (2019). “Personalized safety instruction system for
construction site based on internet technology.” Safety science, 116, pp.161-169.
Tang, S., Shelden, D. R., Eastman, C. M., Pishdad-Bozorgi, P., and Gao, X. (2019). “A review of
building information modeling (BIM) and the internet of things (IoT) devices integration:
Present status and future trends.” Automation in Construction, 101, pp.127-139.
Tavares, P., Costa, C. M., Rocha, L., Malaca, P., Costa, P., Moreira, A. P., Sousa, A., and Veiga,
G. (2019). “Collaborative welding system using BIM for robotic reprogramming and spatial
augmented reality.” Automation in Construction, 106, p.102825.
Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., and Bowman, D. (2016). “Application of
machine learning to construction injury prediction.” Automation in construction, 69, pp.102-
Zagvozda, M., Dimter, S., Moser, V., and Barišić, I. (2019). “Application of GIS technology in
pavement management systems.” Građevinar, 71(04.), pp.297-304.
Zhao, X., Zuo, J., Wu, G., and Huang, C. (2019). “A bibliometric review of green building
research 2000–2016.” Architectural Science Review, 62(1), pp.74-88.
Construction Research Congress 2022 992
Construction Research Congress 2022
Downloaded from by University of Liverpool on 03/09/22. Copyright ASCE. For personal use only; all rights reserved.
... Based on the objectives of this study, the first step consisted of collecting data from previous studies using C4.0 for SD. Structured searches for primary studies were carried out in the main collection of the Scopus and Web of Science Core Collection (WoSCC) databases, as they are the largest citation and abstract databases of the peer-reviewed literature that provides an overview of the world's high-quality research output in the fields of engineering, the environment, etc. [7,30]. ...
Full-text available
The construction industry utilizes a substantial number of resources, which has negative impacts on both environmental and socioeconomic aspects. Therefore, it is important to reduce these negative impacts and maintain sustainable development (SD). Recent studies suggest that integrating Industry 4.0 (also called Construction 4.0 (C4.0) in the construction industry) and SD may help address these concerns, which is a new and ever-evolving field. In order to fully understand SD in the C4.0 context, this paper offers a verifiable and reproducible systematic literature review and bibliometric analysis of associated topics. Through a review of 229 works, this article presents the publication trend, the most prolific journals, countries, institutions, researchers, and keywords analysis , as well as the content analysis of C4.0 impacts on SD based on triple-bottom-line (TBL) dimensions. The authors also identify and summarize the critical success factors (CSFs) of C4.0 toward SD. Overall, findings reveal the potential benefits of C4.0 on SD and contribute to the evaluation of sustainable C4.0 innovations. The key topics and CSFs identified in this work could potentially serve as the basis for future investigations, encouraging and directing interested researchers, and thus supporting both theoretical and practical progress in this evolving research area.
... In today's business, Industry 4.0 is driven by digital transformation (DT) in vertical and horizontal value chains and product or service offerings of firms (Ustundag and Cevikcan, 2017). The implementation of DT often encompasses a variety of digital technologies and applications such as robotics, augmented reality (AR), building information modeling (BIM), 3D printing, cloud computing, and the Internet of things (IoT) (Wang et al., 2022a). In pursuing operational and productivity gains, digital technologies have been widely used in various industries such as manufacturing, aeronautics, and automotive. ...
Full-text available
The purpose of this study is to systematically identify, assess, and categorise the barriers to digital transformation (DT) in the engineering and construction sectors, and thus to better understand their impact and how they might be overcome. This study adopted a sequential mixed qualitative and quantitative data collection and analysis approach. DT barriers were first identified from relevant literature and verified by an expert panel. Then, a questionnaire survey assessing the impacts of the identified DT barriers was distributed to construction professionals in China, and 192 valid responses were retrieved. Further, the data obtained were analysed using ranking analysis, exploratory factor analysis (EFA), and partial least squares-structural equation modelling (PLS-SEM). Based on the ranking analysis, the top three barriers are “lack of industry-specific standards and laws,” “lack of clear vision, strategy and direction for DT,” and “lack of support from top management for DT.” EFA enabled the grouping of the 26 barriers into three categories: (1) lack of laws and regulations (LLR), (2) lack of support and leadership (LSL), and (3) lack of resources and professionals (LRP). The PLS-SEM analysis revealed that LLR, LSL, and LRP were found to have significant negative impacts on DT. These findings contribute to the body of knowledge on DT in the construction industry, and help construction firms and government bodies improve the understanding of these barriers to DT and put forward relevant policies and incentives, thus seizing the DT benefits as a way to enhance construction project management.
... When companies fear that investing in new tools or technology may jeopardize their financial stability, they will be hesitant to do so. Notably, most SMEs are still fearful of shifting to BIM working mode and lack interest in investing in BIM and collaboration tools because of the unclear return on investments (ROI) (Chan et al. 2019;Wang et al. 2022). Some SMEs prefer to outsource their task to another organization in a BIM-enabled project, leading to unproductive collaboration among the different teams (Sacks et al. 2018). ...
Full-text available
As one of the emerging digital technologies, building information modeling (BIM) has been increasingly used in the architecture, engineering, and construction (AEC) industry of many countries. One of the core benefits associated with BIM is facilitating collaboration among project teams, thereby improving information sharing and project performance by providing BIM-based construction networks (BbCNs). Nevertheless, it is still challenging to ensure an effective collaboration process in BIM-enabled projects. Several studies have investigated the factors that influence collaboration in BIM-enabled projects; however, most of these studies focus on technical matters of BIM, while the perspectives of socio-organization and processes have received less attention and are still in the conceptual stage, especially for the role of people management. Thus, an exploratory study was proposed to explore how key factors influence the professionals ’ willingness to collaborate in BIM-enabled projects. A total of 10 hypotheses were established based on literature review and expert verification. Following that, a questionnaire survey was administered in China to solicit the opinions of 273 BIM professionals for data collection. The hypotheses were then examined on the basis of the predictive capacity of regression analysis (binary logistic regression), with the findings validated using neural network analysis (multilayer perceptron). It was found that seven independent variables have a statistically significant influence on professionals’ willingness to collaborate in BIM-enabled projects, namely, professional knowledge, skill, training, investment, BIM tools, BIM ownership, and standards and regulations. Among them, the variable of professional knowledge was ranked as the most influential factor. While extending the existing knowledge of literature, the findings of this study also deliver insights for stakeholders by enhancing their understanding of the BIM collaboration process and its influence factors.
... Additionally, Lu et al. (2019) observed that BIM remains in the pre-adoption stages for most construction organizations, and a vast majority of new technologies, including 3D laser scanning and mobile computing, remain underutilised and unpopular throughout the industry. This slow pace of technology adoption has been widely attributed to resistance to change by industry stakeholders (Abubakar et al. 2014;Davis 2004;Davis and Songer 2002;Ezcan et al. 2020;Gore 2010;Lu et al. 2015;Olawumi et al. 2018;Wang et al. 2021). Change is inevitable and new technologies with prospective applications in the construction industry will continue to spring up in today's ever-changing environment. ...
Conference Paper
Full-text available
People’s resistance to change has been identified as one of the critical challenges that inhibit the promotion and implementation of new technologies in the construction industry. Researchers have propounded various strategies to mitigate people’s resistance to technological change. However, most of these assertions are based on the researchers’ opinions which should be supported by qualitative evidence. Accordingly, this research adopted a Delphi Survey to solicit the views of a purposively sampled 7member expert panel assessing the effectiveness of resistance-mitigating strategies. The results highlighted “Training” as the most effective mitigating strategy, followed consecutively by “organisational support”, “enhancing system design”, “education”, “communication”, “participation”, “peer support”, and “incentivisation”. Notwithstanding this ranking, the research identified that no single mitigating strategy is optimal for completely curbing resistance. Therefore, this research also provided a framework for selecting the appropriate mitigating strategies according to different resistance scenarios. The findings of this study can better equip stakeholders of the construction industry in handling resistance to technological change.
Full-text available
Over the last decade, particular interest in using state-of-the-art emerging technologies for inspection, assessment, and management of civil infrastructures has remarkably increased. Advanced technologies, such as laser scanners, have become a suitable alternative for labor intensive, expensive, and unsafe traditional inspection and maintenance methods, which encourage the increasing use of this technology in construction industry, especially in bridges. This paper aims to provide a thorough mixed scientometric and state-of-the-art review on the application of terrestrial laser scanners (TLS) in bridge engineering and explore investigations and recommendations of researchers in this area. Following the review, more than 1500 research publications were collected, investigated and analyzed through a twofold literature search published within the last decade from 2010 to 2020. Research trends, consisting of dominated sub-fields, co-occurrence of keywords, network of researchers and their institutions, along with the interaction of research networks, were quantitatively analyzed. Moreover, based on the collected papers, application of TLS in bridge engineering and asset management was reviewed according to four categories including (1) generation of 3D model, (2) quality inspection, (3) structural assessment, and (4) bridge information modeling (BrIM). Finally, the paper identifies the current research gaps, future directions obtained from the quantitative analysis, and in-depth discussions of the collected papers in this area.
Full-text available
The implementation of various technologies typically produces a considerable amount of digital data for transportation agencies; therefore, it is desirable to have electronic document management (EDM) systems to promote efficient data-sharing among stakeholders. Although EDM systems have been implemented in the building construction industry and their benefits are well understood, they are not as common in the transportation design and construction industry. There are only a few agencies that have broadly implemented EDM with limited research having been conducted on this topic. Accordingly, the purpose of this paper is to present and analyze the current state of the EDM system implementation within leading US transportation agencies and to develop a framework for the selection and implementation of EDM systems for other agencies. This article describes four case studies that were conducted with four transportation agencies in the US who are at the frontiers in the implementation of EDM systems. Current EDM practices at these agencies as well as the associated benefits and challenges were documented. By comparing and contrasting these case studies, the common functionalities and unique characteristics of various EDM systems were summarized and a framework was developed to guide transportation agencies with the selection and implementation of EDM systems.
Full-text available
The construction industry is a major economic sector, but it is plagued with inefficiencies and low productivity. Robotics and automated systems have the potential to address these shortcomings; however, the level of adoption in the construction industry is very low. This paper presents an investigation into the industry-specific factors that limit the adoption in the construction industry. A mixed research method was employed combining literature review, qualitative and quantitative data collection and analysis. Three focus groups with 28 experts and an online questionnaire were conducted. Principal component and correlation analyses were conducted to group the identified factors and find hidden correlations. The main identified challenges were grouped into four categories and ranked in order of importance: contractor-side economic factors, client-side economic factors, technical and work-culture factors, and weak business case factors. No strong correlation was found among factors. This study will help stakeholders to understand the main industry-specific factors limiting the adoption of robotics and automated systems in the construction industry. The presented findings will support stakeholders to devise mitigation strategies.
Full-text available
The digital revolution is expected to play a decisive role in the transformation of the construction industry, opening new markets, creating new products and boosting its productivity and efficiency. The rapid expansion of advanced technologies, such as Internet of Things (IoT), Building Information Modeling (BIM) and other digital systems are about to update the construction sector. The construction industry can take advantage on new technologies, materials and processes to promote customization and safety, reducing construction waste, time and costs. Advanced technologies and innovative processes commonly used in the manufacturing area are being exported for construction and architectural applications. This paper focuses on the digital transformation of the construction sector, particularly the concept of additive manufacturing (AM) in construction. It describes the main AM technologies being developed for construction, as well its major challenges and opportunities. It concludes that the development of new construction materials for AM requires further research in terms of materials testing and characterization standards, as well the incorporation of building codes and standards. Keywords: Additive manufacturing; Building Information Modeling; Construction industry; Digitization; Construction 4.0; 3D printing; Concrete printing; Digital construction
Full-text available
Over the last few years, communication latency has been a major hurdle for most of the applications deployed in different network domains. During this era, a number of communication protocols and standards were developed and used by the community. However, still the problem of latency persists keeping in view of the Quality of Service (QoS) and Quality of Experience (QoE) for different applications. To mitigate the aforementioned issues, in this paper, we present an in-depth survey of state-of-the art proposals having Tactile Internet as a backbone for delay mitigation using 5G networks for future ultra reliable low latency applications such as-Healthcare 4.0, Industry 4.0, Virtual Reality and Augmented Reality and smart Education. From the existing proposals, it has been observed that Tactile Internet can provide interactions between virtual objects to givea feel of real environment with maximum latency of 1ms. Also, this paper highlights the key differences between the Tactile Internet and Internet of Things in context with 5G revolution. Then, open issues and challenges of Tactile Internet for smart applications are analyzed. Finally, a comparison of existing proposals with respect to various parameters is presented which allows the end users to select one of the proposals in comparison to its merits over the others.
Ensuring the resiliency of a nation’s transportation infrastructure network requires a skilled and dependable workforce; this relies heavily on a transportation agency’s ability to recruit and retain their employees. Public transportation agencies face unprecedented challenges in preserving the workforce necessary to function effectively and need robust strategies to maintain workforce resiliency by recruiting and retaining quality staff to construct and maintain transportation infrastructure now and into the future. This study identified existing workforce issues and recruitment and retention practices by state Departments of Transportation (DOTs) and compared rural and urban transportation employee outlooks. Results indicated that the primary incentives for current DOT employees to consider private-sector employment are better salary and promotional opportunities. Additionally, rural DOT regions have a more difficult time recruiting and retaining employees, and rural DOT employees feel that DOTs are not fully aware of their needs. This study contributes to the body of knowledge by providing a means to assess and ultimately improve workforce resiliency in public transportation agencies, thereby allowing management to maintain transportation infrastructure successfully.
With the extensive adoption of artificial intelligence (AI), construction engineering and management (CEM) is experiencing a rapid digital transformation. Since AI-based solutions in CEM has become the current research focus, it needs to be comprehensively understood. In this regard, this paper presents a systematic review under both scientometric and qualitative analysis to present the current state of AI adoption in the context of CEM and discuss its future research trends. To begin with, a scientometric review is performed to explore the characteristics of keywords, journals, and clusters based on 4,473 journal articles published in 1997-2020. It is found that there has been an explosion of relevant papers especially in the past 10 years along with the change in keyword popularity from expert systems to building information modeling (BIM), digital twins, and others. Then, a brief understanding of CEM is provided, which can be benefited from the emerging trend of AI in terms of automation, risk mitigation, high efficiency, digitalization, and computer vision. Special concerns have been put on six hot research topics that amply the advantage of AI in CEM, including (1) knowledge representation and reasoning, (2) information fusion, (3) computer vision, (4) natural language processing, (5) intelligence optimization, and (6) process mining. The goal of these topics is to model, predict, and optimize issues in a data-driven manner throughout the whole lifecycle of the actual complex project. To further narrow the gap between AI and CEM, six key directions of future researches, such as smart robotics, cloud virtual and augmented reality (cloud VR/AR), Artificial Intelligence of Things (AIoT), digital twins, 4D printing, and blockchains, are highlighted to constantly facilitate the automation and intelligence in CEM.
With the outbreak of the 2019 novel coronavirus (COVID-19) epidemic in Wuhan, China, in January 2020, the escalating number of confirmed and suspected cases overwhelmed the admission capacity of the designated hospitals. Two specialty field hospitals—Huoshenshan and Leishenshan—were designed, built and commissioned in record time (9–12 days) to address the outbreak. This study documents the design and construction of Leishenshan Hospital. Based on data collected from various sources such as the semi-structured interviews of key stakeholders from Leishenshan Hospital, this study found that adhering to a product, organization, and process (POP) modeling approach combined with building information modeling (BIM) allowed for the ultra-rapid creation, management, and communication of project-related information, resulting in the successful development of this fully functional, state-of-the-art infectious disease specialty hospital. With the unfortunate ongoing international COVID-19 outbreak, many countries and regions face similar hospital capacity problems. It is thus expected that the lessons learned from the design, construction and commissioning of Leishenshan Hospital can provide a valuable reference to the development of specialty field hospitals in other countries and regions.
The majority of construction fatalities and accidents in Chile occur in high-rise building construction projects that also suffer from an insufficient number of safety managers on-site. Unmanned aerial vehicles (UAVs) and their generated aerial visual contents have the potential to help the limited number of safety managers in such projects to quickly and properly inspect the inaccessible, hard-to-reach, or unsafe locations on the site and to enhance their safety assessment of those projects. In this study, a case study approach was adopted to investigate how UAV technology and their generated aerial visual contents might affect the current approach of conducting safety planning and monitoring in a high-rise building construction site in Chile with a limited number of safety managers. The case study involved three steps: (1) understanding the current safety planning and monitoring process in a high-rise construction project, (2) investigating how UAV-related tasks and generated visual contents could be integrated into the current process, and (3) assessment of how such UAV integration might affect the current safety planning and monitoring process. The outcome of the case study provided a detailed overview of the new steps required to integrate UAVs in the current safety planning and monitoring process and concluded that adoption of UAV technology had enhanced identification and assessment of hazards in the high-rise project. Hazards associated with unsafe acts and conditions at height (e.g., missing guardrails or safety nets around unprotected edges or openings and loose or unsecured material at height) were the most common types of hazards identified using the UAV in this case study. Safety managers in the project also rated aerial videos captured by the UAV as the most useful type of data for their safety planning and monitoring tasks. The UAV also significantly reduced the amount of time required for safety managers to conduct their site visit walkthroughs on the project site.
The optimization of the information flow from the initial design and through the several production stages plays a critical role in ensuring product quality while also reducing the manufacturing costs. As such, in this article we present a cooperative welding cell for structural steel fabrication that is capable of leveraging the Building Information Modeling (BIM) standards to automatically orchestrate the necessary tasks to be allocated to a human operator and a welding robot moving on a linear track. We propose a spatial augmented reality system that projects alignment information into the environment for helping the operator tack weld the beam attachments that will be later on seam welded by the industrial robot. This way we ensure maximum flexibility during the beam assembly stage while also improving the overall productivity and product quality since the operator no longer needs to rely on error prone measurement procedures and he receives his tasks through an immersive interface, relieving him from the burden of analyzing complex manufacturing design specifications. Moreover, no expert robotics knowledge is required to operate our welding cell because all the necessary information is extracted from the Industry Foundation Classes (IFC), namely the CAD models and welding sections, allowing our 3D beam perception systems to correct placement errors or beam bending, which coupled with our motion planning and welding pose optimization system ensures that the robot performs its tasks without collisions and as efficiently as possible while maximizing the welding quality.